{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from ggplot import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ggplot: (274955645)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KTr83W7ZsUXp6uvr06aOvv/661flcLlez39etW6fS0tJm0woLC1VUVGSkThyfUChk6fYr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\n9tEJof4wiAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10637de50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ggplot(mpg, aes(x='cty')) + \\\n",
    "    geom_histogram() + \\\n",
    "    xlab(\"City MPG (Miles per Gallon)\") + \\\n",
    "    ylab(\"# of Obs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
